Abstract
Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. Although diffusion models have achieved remarkable progress in recent years, they have limitations in the unpaired image-to-image translation tasks due to the Gaussian prior assumption. Schrödinger Bridge (SB), which learns an SDE to translate between two arbitrary distributions, have risen as an attractive solution to this problem. However, none of SB models so far have been successful at unpaired translation between high-resolution images. In this work, we propose the Unpaired Neural Schrödinger Bridge (UNSB), which combines SB with adversarial training and regularization to learn a SB between unpaired data. We demonstrate that UNSB is scalable, and that it successfully solves various unpaired image-to-image translation tasks. Code: \url{this https URL}
Abstract (translated)
扩散模型是一种强大的生成模型，模拟随机微分方程(SDEs)从噪声中生成数据。尽管扩散模型近年来取得了显著进展，但由于高分辨率图像之间的非配对翻译任务依赖于高斯先验假设，它们在这些任务中具有限制。肖莱姆桥(SB)是一种学习两个任意分布之间的SDEs的模型，因此成为解决这个问题的一种有吸引力的解决方案。然而，迄今为止，所有SB模型都没有在配对高分辨率图像之间的非配对翻译任务中成功实现。在本文中，我们提出了未配对神经网络肖莱姆桥(UNSB)，它将SB与对抗训练和正则化相结合，以学习配对数据之间的UNSB。我们证明了UNSB是可扩展的，并且它成功地解决了各种配对图像到图像翻译任务。代码： \url{this https URL}
URL
https://arxiv.org/abs/2305.15086
